One-Page Executive Brief: Autonomous Analytics Platform

Problem

Today, analytics implementations are:

Fragmented: 5 different approaches across teams (satellite.track, dataLayer, custom helpers)
Error-prone: Manual PV interpretation, inconsistent implementation
Slow: New market rollout = 2-3 days of engineering work
Risky: One shared code fix breaks 3 different markets

Cost: 1-2 regressions per month, 20-30 engineering hours per feature event.

Solution

Build an Autonomous Analytics Delivery Platform where:

You give a simple business request
An AI agent generates contracts, code, and tests
System validates everything automatically
PR ships with zero manual coding

Result: Feature event from prompt to production in ~10 minutes instead of 2-3 days.

How It Works (3 Steps)

STEP 1: You Provide a Simple Prompt
"Add analytics for search filters in ZA and CA markets.
Track apply, clear, reset with filter ID and result count."

     ↓

STEP 2: Agent Asks Clarifying Questions (If Needed)
"1. Debounce events? 2. Same field names? 3. Dispatch method?"
(You answer in 1 line each)

     ↓

STEP 3: Agent Delivers Ready-to-Merge PR
✓ Event contracts + versioning
✓ Generated SDK code in app
✓ Unit tests + E2E payload snapshots
✓ Market-specific policy config
✓ Complete test evidence

You: Approve (2-minute scan) → Merge → Done

What Changes

Before	After
Manual PV interpretation	Automated contract generation
Custom code in 3-4 places	One unified SDK call
Market hardcoded in app logic	Configuration-driven overlays
Manual test writing	Generated + validated tests
Regression detected in QA	Auto-detected in production

Implementation Roadmap

Phase	Duration	Effort	You Get
1: Foundation	2 weeks	Med	Contract + SDK framework
2: Consolidation	2 weeks	Med	All teams on SDK
3: Agent MVP	4 weeks	High	Prompt → PR automation
4: Production Ready	2 weeks	Low	Monitoring + governance
Total	10 weeks	~4.5 FTE	Full autonomy

Business Impact

Metrics (Target vs Today)

Metric	Today	Target	Improvement
Time per feature event	4-6 hours	10 minutes	95% reduction
New market rollout	2-3 days	2 hours	97% reduction
Regression rate	2-3/month	<1/month	75% reduction
Manual touchpoints	100%	5%	95% reduction
Engineering hours/year	~500 hours	~25 hours	95% reduction

Risk Management

Risk: Agent generates incorrect code
Mitigation: 6-month manual review gate; auto-accuracy monitoring

Risk: Production regression
Mitigation: Drift detection catches issues <1 hour; auto-rollback if SLO breaks

Risk: Breaking analytics changes across markets
Mitigation: Mandatory semver for app-owned contracts, compatibility gate in CI, canary-first rollout, event kill-switch

Risk: Team adoption
Mitigation: Require all new analytics use agent; show time savings

Decision Required

Approve 10-week initiative to:

Build Autonomous Analytics Platform
Consolidate 5 teams onto unified SDK
Deploy AI agent for prompt → PR automation
Measure success: 95% reduction in manual analytics effort

Investment: ~4.5 FTE for 10 weeks
Return: 500+ engineering hours saved annually, fewer regressions, faster market launches

Next Steps

Week 1: Kickoff, team allocation, Phase 1 begins
Week 2: Foundation framework live
Week 3-4: Teams migrated to SDK
Week 8: Agent MVP ready for pilot
Week 10: Production launch

Questions?

How does this work with existing Adobe Launch setup?
→ We wrap Adobe Launch, don't replace it. Contract layer sits on top.
What if requirements change mid-implementation?
→ Agent regenerates contracts and code. Existing events untouched.
Do we have to migrate all teams at once?
→ No. Wave-by-wave migration; old and new paths coexist during transition.
What if agent gets it wrong?
→ Manual review gate for 6 months; governance gates catch policy violations; drift detection alerts on production anomalies.
How long until full autonomy?
→ By week 10, system is production-ready. Month 2-3, team gets comfortable and governance gates can relax.